Various methods have recently been developed that utilize the global positioning system (GPS), radio-frequency identification (RFID), an image sensor, a laser radar or the like, for acquiring location information of a subject. By recording the location information by such methods, the location where the subject was at a certain time in the past can be identified.
For example, studies are being made on the utilization of records of a plurality of pieces of location information (time series data of the location information) each associated with time-of-day information, as a life log of the subject. In some studies, specifically, a part of the location information constituting the life log, the part representing physically close locations, is selectively grouped so as to classify the life log by events that are significant to the user. Such classification facilitates searching of the past life log of the subject.
Now, the movement pattern of the subject seen in the life log widely varies depending on the purpose. When the purpose of the activity of the subject is “sight-seeing” or “strolling” for example, the time series data of the relevant location information concentrates in a relatively small range. However, when the purpose of the activity is “transfer” from one place to another, the time series data of the relevant location information is recorded as data showing a movement toward a specific direction.
To classify the time series data of the location information, simply grouping the location information representing physically close locations is not suitable. For example, the location information acquired during the “transfer” represents locations that are physically distant from each other, and therefore such information is not classified into a single group of “transfer”, but into a plurality of groups (fragmented).
On the other hand, a statistical model can be utilized for classification into groups each representing activities of a similar pattern. For example, PTL 1 proposes processing the time series data of the location information on the basis of a hidden Markov model (HMM), thereby classifying the time series data into units of the location information that presents similar statistical dispersion.